docs: def _apply_adjustments(COEFFS, C_ADJ, tau_ss, mean, stddevs, sites, rup, dists,
                         imt, stddev_types, log_phi_ss, NL=None, tau_value=None):
      """
      This method applies adjustments to the mean and standard deviation.
      The small-magnitude adjustments are applied to mean, whereas the
      embeded single station sigma logic tree is applied to the
      total standard deviation.
      """
      c1_dists = _compute_C1_term(C_ADJ, dists)
      phi_ss = _compute_phi_ss(
          C_ADJ, rup.mag, c1_dists, log_phi_ss, C_ADJ['mean_phi_ss']
      )
  
      mean_corr = np.exp(mean) * C_ADJ['k_adj'] * \
          _compute_small_mag_correction_term(C_ADJ, rup.mag, dists)
  
      mean_corr = np.log(mean_corr)
  
      std_corr = _get_corr_stddevs(COEFFS[imt], tau_ss, stddev_types,
                                   len(sites.vs30), phi_ss, NL, tau_value)
  
      stddevs = np.array(std_corr)
  
      return mean_corr, stddevs